Seismic Stratigraphic Interpretation Based on Deep Active Learning

Gu, Xiaofeng; Lu, Wenkai; Ao, Yile; Li, Yinshuo; Song, Cao

doi:10.1109/tgrs.2023.3288737

Cited by 4 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We update the parameter matrix W using the standard backpropagation technique. Specifically, if L denotes the loss function defined in Equation ( 6), then the derivative of W with respect to Equation ( 6) is calculated as indicated in Equation (7).…”

Section: Fine-tuning Of the Deep Convolutional Neural Network Modelmentioning

confidence: 99%

“…∂z j (x i ;W) ∂W (7) where z j (x i ; W) m j=1 represents the activations for all samples of i at the final layer of the CNN before the SoftMax.…”

Section: Fine-tuning Of the Deep Convolutional Neural Network Modelmentioning

confidence: 99%

“…Active learning strategies are essential for deep learning and adapting them is a significant advancement [7]. Techniques such as uncertainty sampling and query-bycommittee can now be extended to deep neural networks [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Integrated Active Deep Learning Approach for Image Classification from Unlabeled Data with Minimal Supervision

Abdelwahab,

Afifi,

Salama

2023

Electronics

View full text Add to dashboard Cite

The integration of active learning (AL) and deep learning (DL) presents a promising avenue for enhancing the efficiency and performance of deep learning classifiers. This article introduces an approach that seamlessly integrates AL principles into the training process of DL models to build robust image classifiers. The proposed approach employs a unique methodology to select high-confidence unlabeled data points for immediate labeling, reducing the need for human annotation and minimizing annotation costs. Specifically, by combining uncertainty sampling with the pseudo-labeling of confident data, the proposed approach expands the training set efficiently. The proposed approach uses a hybrid active deep learning model that selects the most informative data points that need labeling based on an uncertainty measure. Then, it iteratively retrains a deep neural network classifier on the newly labeled samples. The model achieves high accuracy with fewer manually labeled samples than traditional supervised deep learning by selecting the most informative samples for labeling and retraining in a loop. Experiments on various image classification datasets demonstrate that the proposed model outperforms conventional approaches in terms of classification accuracy and reduced human annotation requirements. The proposed model achieved accuracy of 98.9% and 99.3% for the Cross-Age Celebrity and Caltech Image datasets compared to the conventional approach, which achieved 92.3% and 74.3%, respectively. In summary, this work presents a promising unified active deep learning approach to minimize the human effort in manually labeling data while maximizing classification accuracy by strategically labeling only the most valuable samples for the model.

show abstract

Section: Fine-tuning Of the Deep Convolutional Neural Network Modelmentioning

confidence: 99%

“…∂z j (x i ;W) ∂W (7) where z j (x i ; W) m j=1 represents the activations for all samples of i at the final layer of the CNN before the SoftMax.…”

Section: Fine-tuning Of the Deep Convolutional Neural Network Modelmentioning

confidence: 99%

See 1 more Smart Citation

An Integrated Active Deep Learning Approach for Image Classification from Unlabeled Data with Minimal Supervision

Abdelwahab,

Afifi,

Salama

2023

Electronics

View full text Add to dashboard Cite

show abstract

Seismic Stratigraphic Interpretation Based on Unsupervised Validation and Spectral Clustering Sampling

Luo,

Gu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

With the continuous development of computer technology and significant improvements in computing power, deep learning has found increasing applications in seismic stratigraphy interpretation, showcasing notable advancements over traditional methods. However, due to the unique characteristics of seismic data, labeling such data has become extremely challenging and time-consuming, necessitating the involvement of professional geologists. Consequently, few-shot learning has garnered considerable attention for seismic image segmentation.Nevertheless, two key challenges remain in few-shot learning: selecting more representative samples and validating the model during training. As the availability of labeled samples decreases, we are left with inadequate data for the validation set. In this paper, instead of solely focusing on enhancing the network structure, we propose the utilization of Spectral Clustering Sampling (SCS) methods for training data selection. Additionally, we introduce a metric called Sum of Different (SD), which can be computed without the need for labeled data, to replace the conventional validation set loss employed in traditional validation approaches. Notably, By employing SCS methods for training data selection and introducing the SD metric to replace traditional validation set loss in F3 dataset, we have achieved remarkable outcomes.

show abstract

Spatial Pattern Learning: Dip Structure Constraint Multi-View Convolutional Neural Network for Pre-Stacked Seismic Inversion

Song,

Lu,

et al. 2023

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Seismic Stratigraphic Interpretation Based on Deep Active Learning

Cited by 4 publications

References 20 publications

An Integrated Active Deep Learning Approach for Image Classification from Unlabeled Data with Minimal Supervision

An Integrated Active Deep Learning Approach for Image Classification from Unlabeled Data with Minimal Supervision

Seismic Stratigraphic Interpretation Based on Unsupervised Validation and Spectral Clustering Sampling

Spatial Pattern Learning: Dip Structure Constraint Multi-View Convolutional Neural Network for Pre-Stacked Seismic Inversion

Contact Info

Product

Resources

About